Amazon Kinesis vs Amazon Kinesis Firehose vs Google Cloud Dataflow

Amazon Kinesis
Amazon Kinesis

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Amazon Kinesis Firehose
Amazon Kinesis Firehose

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Google Cloud Dataflow
Google Cloud Dataflow

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- No public GitHub repository available -
- No public GitHub repository available -
- No public GitHub repository available -

What is Amazon Kinesis?

Amazon Kinesis can collect and process hundreds of gigabytes of data per second from hundreds of thousands of sources, allowing you to easily write applications that process information in real-time, from sources such as web site click-streams, marketing and financial information, manufacturing instrumentation and social media, and operational logs and metering data.

What is Amazon Kinesis Firehose?

Amazon Kinesis Firehose is the easiest way to load streaming data into AWS. It can capture and automatically load streaming data into Amazon S3 and Amazon Redshift, enabling near real-time analytics with existing business intelligence tools and dashboards you’re already using today.

What is Google Cloud Dataflow?

Google Cloud Dataflow is a unified programming model and a managed service for developing and executing a wide range of data processing patterns including ETL, batch computation, and continuous computation. Cloud Dataflow frees you from operational tasks like resource management and performance optimization.

Want advice about which of these to choose?Ask the StackShare community!

Why do developers choose Amazon Kinesis?
Why do developers choose Amazon Kinesis Firehose?
Why do developers choose Google Cloud Dataflow?
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        What are the cons of using Amazon Kinesis?
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        What are the cons of using Google Cloud Dataflow?
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              What companies use Amazon Kinesis?
              What companies use Amazon Kinesis Firehose?
              What companies use Google Cloud Dataflow?
              What are some alternatives to Amazon Kinesis, Amazon Kinesis Firehose, and Google Cloud Dataflow?
              Kafka
              Kafka is a distributed, partitioned, replicated commit log service. It provides the functionality of a messaging system, but with a unique design.
              Apache Spark
              Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
              Amazon SQS
              Transmit any volume of data, at any level of throughput, without losing messages or requiring other services to be always available. With SQS, you can offload the administrative burden of operating and scaling a highly available messaging cluster, while paying a low price for only what you use.
              See all alternatives
              What tools integrate with Amazon Kinesis?
              What tools integrate with Amazon Kinesis Firehose?
              What tools integrate with Google Cloud Dataflow?
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                  Decisions about Amazon Kinesis, Amazon Kinesis Firehose, and Google Cloud Dataflow
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                  Interest over time
                  Reviews of Amazon Kinesis, Amazon Kinesis Firehose, and Google Cloud Dataflow
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                  How developers use Amazon Kinesis, Amazon Kinesis Firehose, and Google Cloud Dataflow
                  Avatar of Luca Bianchi
                  Luca Bianchi uses Amazon KinesisAmazon Kinesis

                  Fast data stream maanagement hiding complexity

                  Avatar of KASA FIK s.r.o.
                  KASA FIK s.r.o. uses Amazon KinesisAmazon Kinesis

                  Event streaming

                  How much does Amazon Kinesis cost?
                  How much does Amazon Kinesis Firehose cost?
                  How much does Google Cloud Dataflow cost?
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